code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--env",
          type=str,
          default="BreakoutNoFrameskip-v4",
          help="OpenAI Atari domain to perform algorithm on.",
      )
      parser.add_argument(
          "--outdir",
          type=str,
          default="results",
          help=(
              "Directory path to save output files."
              " If it does not exist, it will be created."
          ),
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 31)"
      )
      parser.add_argument(
          "--gpu",
          type=int,
          default=0,
          help="GPU to use, set to -1 if no GPU.",
      )
      parser.add_argument("--demo", action="store_true", default=False)
      parser.add_argument("--load", type=str, default=None)
      parser.add_argument(
          "--final-exploration-frames",
          type=int,
          default=10**6,
          help="Timesteps after which we stop "
          + "annealing exploration rate",
      )
      parser.add_argument(
          "--final-epsilon",
          type=float,
          default=0.01,
          help="Final value of epsilon during training.",
      )
      parser.add_argument(
          "--eval-epsilon",
          type=float,
          default=0.001,
          help="Exploration epsilon used during eval episodes.",
      )
      parser.add_argument("--noisy-net-sigma", type=float, default=None)
      parser.add_argument(
          "--arch",
          type=str,
          default="doubledqn",
          choices=["nature", "nips", "dueling", "doubledqn"],
          help="Network architecture to use.",
      )
      parser.add_argument(
          "--steps",
          type=int,
          default=5 * 10**7,
          help="Total number of timesteps to train the agent.",
      )
      parser.add_argument(
          "--max-frames",
          type=int,
          default=30 * 60 * 60,  # 30 minutes with 60 fps
          help="Maximum number of frames for each episode.",
      )
      parser.add_argument(
          "--replay-start-size",
          type=int,
          default=5 * 10**4,
          help="Minimum replay buffer size before "
          + "performing gradient updates.",
      )
      parser.add_argument(
          "--target-update-interval",
          type=int,
          default=3 * 10**4,
          help="Frequency (in timesteps) at which "
          + "the target network is updated.",
      )
      parser.add_argument(
          "--eval-interval",
          type=int,
          default=10**5,
          help="Frequency (in timesteps) of evaluation phase.",
      )
      parser.add_argument(
          "--update-interval",
          type=int,
          default=4,
          help="Frequency (in timesteps) of network updates.",
      )
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument(
          "--no-clip-delta", dest="clip_delta", action="store_false"
      )
      parser.add_argument("--num-step-return", type=int, default=1)
      parser.set_defaults(clip_delta=True)
      parser.add_argument(
          "--agent",
          type=str,
          default="DoubleDQN",
          choices=["DQN", "DoubleDQN", "PAL"],
      )
      parser.add_argument(
          "--log-level",
          type=int,
          default=20,
          help="Logging level. 10:DEBUG, 20:INFO etc.",
      )
      parser.add_argument(
          "--render",
          action="store_true",
          default=False,
          help="Render env states in a GUI window.",
      )
      parser.add_argument(
          "--monitor",
          action="store_true",
          default=False,
          help=(
              "Monitor env. Videos and additional information are saved as output files."
          ),
      )
      parser.add_argument(
          "--lr", type=float, default=2.5e-4, help="Learning rate."
      )
      parser.add_argument(
          "--prioritized",
          action="store_true",
          default=False,
          help="Use prioritized experience replay.",
      )
      parser.add_argument(
          "--checkpoint-frequency",
          type=int,
          default=None,
          help="Frequency at which agents are stored.",
      )
      args = parser.parse_args()
  
      import logging
  
      logging.basicConfig(level=args.log_level)
  
      # Set a random seed used in PFRL.
      utils.set_random_seed(args.seed)
  
      # Set different random seeds for train and test envs.
      train_seed = args.seed
      test_seed = 2**31 - 1 - args.seed
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
      print("Output files are saved in {}".format(args.outdir))
  
      def make_env(test):
          # Use different random seeds for train and test envs
          env_seed = test_seed if test else train_seed
          env = atari_wrappers.wrap_deepmind(
              atari_wrappers.make_atari(
                  args.env, max_frames=args.max_frames
              ),
              episode_life=not test,
              clip_rewards=not test,
          )
          env.seed(int(env_seed))
          if test:
              # Randomize actions like epsilon-greedy in evaluation as well
              env = pfrl.wrappers.RandomizeAction(
                  env, args.eval_epsilon
              )
          if args.monitor:
              env = pfrl.wrappers.Monitor(
                  env,
                  args.outdir,
                  mode="evaluation" if test else "training",
              )
          if args.render:
              env = pfrl.wrappers.Render(env)
          return env
  
      env = make_env(test=False)
      eval_env = make_env(test=True)
  
      n_actions = env.action_space.n
      q_func = parse_arch(args.arch, n_actions)
  
      if args.noisy_net_sigma is not None:
          pnn.to_factorized_noisy(
              q_func, sigma_scale=args.noisy_net_sigma
          )
          # Turn off explorer
          explorer = explorers.Greedy()
      else:
          explorer = explorers.LinearDecayEpsilonGreedy(
              1.0,
              args.final_epsilon,
              args.final_exploration_frames,
              lambda: np.random.randint(n_actions),
          )
  
      # Use the Nature paper's hyperparameters
      opt = pfrl.optimizers.RMSpropEpsInsideSqrt(
          q_func.parameters(),
          lr=args.lr,
          alpha=0.95,
          momentum=0.0,
          eps=1e-2,
          centered=True,
      )
  
      # Select a replay buffer to use
      if args.prioritized:
          # Anneal beta from beta0 to 1 throughout training
          betasteps = args.steps / args.update_interval
          rbuf = replay_buffers.PrioritizedReplayBuffer(
              10**6,
              alpha=0.6,
              beta0=0.4,
              betasteps=betasteps,
              num_steps=args.num_step_return,
          )
      else:
          rbuf = replay_buffers.ReplayBuffer(
              10**6, args.num_step_return
          )
  
      def phi(x):
          # Feature extractor
          return np.asarray(x, dtype=np.float32) / 255
  
      Agent = parse_agent(args.agent)
      agent = Agent(
          q_func,
          opt,
          rbuf,
          gpu=args.gpu,
          gamma=0.99,
          explorer=explorer,
          replay_start_size=args.replay_start_size,
          target_update_interval=args.target_update_interval,
          clip_delta=args.clip_delta,
          update_interval=args.update_interval,
          batch_accumulator="sum",
          phi=phi,
      )
  
      if args.load:
          agent.load(args.load)
  
      if args.demo:
          eval_stats = experiments.eval_performance(
              env=eval_env,
              agent=agent,
              n_steps=None,
              n_episodes=args.eval_n_runs,
          )
          print(
              "n_runs: {} mean: {} median: {} stdev {}".format(
                  args.eval_n_runs,
                  eval_stats["mean"],
                  eval_stats["median"],
                  eval_stats["stdev"],
              )
          )
      else:
          experiments.train_agent_with_evaluation(
              agent=agent,
              env=env,
              steps=args.steps,
              eval_n_steps=None,
              checkpoint_freq=args.checkpoint_frequency,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              outdir=args.outdir,
              save_best_so_far_agent=False,
              eval_env=eval_env,
          )
  
file_path: pfrl/examples/atari/train_dqn_ale.py
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import pfrl
  from pfrl import agents, experiments, explorers
  from pfrl import nn
  from pfrl import replay_buffers, utils
  from pfrl.initializers import init_chainer_default
  from pfrl.q_functions import DiscreteActionValueHead, DuelingDQN
  from pfrl.wrappers import atari_wrappers
label_desc: Use image node_id 3 for calling a global function with example usage: main() and returns: env, unknown